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Genes, enzymes and chemicals of terpenoid diversity in the constitutive and induced defence of conifers against insects and pathogens*

2006· review· en· W2138694888 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueNew Phytologist · 2006
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsCanada's Michael Smith Genome Sciences Centre
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBiologyHerbivoreTerpenoidInsectResistance (ecology)Chemical defenseChemical ecologyHost (biology)BotanyEcologyTerpene

Abstract

fetched live from OpenAlex

Insects select their hosts, but trees cannot select which herbivores will feed upon them. Thus, as long-lived stationary organisms, conifers must resist the onslaught of varying and multiple attackers over their lifetime. Arguably, the greatest threats to conifers are herbivorous insects and their associated pathogens. Insects such as bark beetles, stem- and wood-boring insects, shoot-feeding weevils, and foliage-feeding budworms and sawflies are among the most devastating pests of conifer forests. Conifer trees produce a great diversity of compounds, such as an enormous array of terpenoids and phenolics, that may impart resistance to a variety of herbivores and microorganisms. Insects have evolved to specialize in resistance to these chemicals -- choosing, feeding upon, and colonizing hosts they perceive to be best suited to reproduction. This review focuses on the plant-insect interactions mediated by conifer-produced terpenoids. To understand the role of terpenoids in conifer-insect interactions, we must understand how conifers produce the wide diversity of terpenoids, as well as understand how these specific compounds affect insect behaviour and physiology. This review examines what chemicals are produced, the genes and proteins involved in their biosynthesis, how they work, and how they are regulated. It also examines how insects and their associated pathogens interact with, elicit, and are affected by conifer-produced terpenoids.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.389
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.046
GPT teacher head0.279
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it